665 citations · 922 across the 15 of their papers we have counts for
16 papers
Domain Generalization in-the-Wild: Disentangling Classification from Domain-Aware Representations
Ha Min Son, Zhe Zhao, Shahbaz Rezaei +1
Evaluating domain generalization (DG) for foundational models like CLIP is challenging, as web-scale pretraining data potentially covers many existing benchmarks. Consequently, cur…
FixCLR: Negative-Class Contrastive Learning for Semi-Supervised Domain Generalization
Ha Min Son, Shahbaz Rezaei, Xin Liu
Semi-supervised domain generalization (SSDG) aims to solve the problem of generalizing to out-of-distribution data when only a few labels are available. Due to label scarcity, appl…
On the Necessity of Multi-Domain Explanation: An Uncertainty Principle Approach for Deep Time Series Models
Shahbaz Rezaei, Avishai Halev, Xin Liu
A prevailing approach to explain time series models is to generate attribution in time domain. A recent development in time series XAI is the concept of explanation spaces, where a…
Explanation Space: A New Perspective into Time Series Interpretability
Shahbaz Rezaei, Xin Liu
Human understandable explanation of deep learning models is essential for various critical and sensitive applications. Unlike image or tabular data where the importance of each inp…
Benchmarking Counterfactual Interpretability in Deep Learning Models for Time Series Classification
Ziwen Kan, Shahbaz Rezaei, Xin Liu
The popularity of deep learning methods in the time series domain boosts interest in interpretability studies, including counterfactual (CF) methods. CF methods identify minimal ch…
On the Discredibility of Membership Inference Attacks
Shahbaz Rezaei, Xin Liu
With the wide-spread application of machine learning models, it has become critical to study the potential data leakage of models trained on sensitive data. Recently, various membe…
User-Level Membership Inference Attack against Metric Embedding Learning
Guoyao Li, Shahbaz Rezaei, Xin Liu
Membership inference (MI) determines if a sample was part of a victim model training set. Recent development of MI attacks focus on record-level membership inference which limits t…
An Efficient Subpopulation-based Membership Inference Attack
Shahbaz Rezaei, Xin Liu
Membership inference attacks allow a malicious entity to predict whether a sample is used during training of a victim model or not. State-of-the-art membership inference attacks ha…
Accuracy-Privacy Trade-off in Deep Ensemble: A Membership Inference Perspective
Shahbaz Rezaei, Zubair Shafiq, Xin Liu
Deep ensemble learning has been shown to improve accuracy by training multiple neural networks and averaging their outputs. Ensemble learning has also been suggested to defend agai…
On the Difficulty of Membership Inference Attacks
Shahbaz Rezaei, Xin Liu
Recent studies propose membership inference (MI) attacks on deep models, where the goal is to infer if a sample has been used in the training process. Despite their apparent succes…
Security of Deep Learning Methodologies: Challenges and Opportunities
Shahbaz Rezaei, Xin Liu
Despite the plethora of studies about security vulnerabilities and defenses of deep learning models, security aspects of deep learning methodologies, such as transfer learning, hav…
Large-scale Mobile App Identification Using Deep Learning
Shahbaz Rezaei, Bryce Kroencke, Xin Liu
Many network services and tools (e.g. network monitors, malware-detection systems, routing and billing policy enforcement modules in ISPs) depend on identifying the type of traffic…
Multitask Learning for Network Traffic Classification
Shahbaz Rezaei, Xin Liu
Traffic classification has various applications in today's Internet, from resource allocation, billing and QoS purposes in ISPs to firewall and malware detection in clients. Classi…
A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer Learning
Shahbaz Rezaei, Xin Liu
Due to insufficient training data and the high computational cost to train a deep neural network from scratch, transfer learning has been extensively used in many deep-neural-netwo…
How to Achieve High Classification Accuracy with Just a Few Labels: A Semi-supervised Approach Using Sampled Packets
Shahbaz Rezaei, Xin Liu
Network traffic classification, which has numerous applications from security to billing and network provisioning, has become a cornerstone of today's computer networks. Previous s…
Deep Learning for Encrypted Traffic Classification: An Overview
Shahbaz Rezaei, Xin Liu
Traffic classification has been studied for two decades and applied to a wide range of applications from QoS provisioning and billing in ISPs to security-related applications in fi…